How UCLA PhD candidate used machine learning to find true love


Just before Chris Mckinley earned his PhD in mathematics from UCLA, he was on a quest for love.  Like millions of others on that quest, he turned to the aid of dating sites.  After countless failed matches and little to no prospects he turned to his skills to help him do the heavy lifting.  Mickinley’s research as a PhD candidate focused on large-scale data processing and parallel numerical methods.  Knowing that one of the dating sites became famous for their mathematical approach to matching couples, he created a few python scripts to data mine potential matches that were more suited to his him that what the site was matching him with.  Using a clustering algorithm based on one developed by Bell Labs (previously used to analyze diseased soybean crops) called K-modes, he was able to reverse-engineer how the dating site weighted the questions it asks each person to gauge how well they match with another.  With this data in hand, McKinley then used a machine-learning called adaptive boosting to weight his own answers to these questions to better match himself with suitors that more accurately fit who he’d like to meet.  Needless to say, his mathematics brought him a fairytale ending.

Found on: Wired.